Modeling of shape memory alloy springs using a recurrent neural network

• Autorzy: Kardan I , Abiri R. , Kabganian M. , Vahabi M.
• Języki publikacji: EN

Abstrakty

EN

In this paper, a recurrent neural network structure is proposed for the modeling of the behavior of shape memory alloy springs. Numerous mathematical modeling and experimental evaluations show that the force exerted by SMAs, aside from their length and applied voltages, depends on the loading path. Therefore, in addition to the applied voltage and deformation, a feedback of the voltage applied to, and the force exerted by the SMA spring in the previous time step is included in the inputs to this neural network to represent the loading path. Fed by adequate inputs, the NN estimates the output force of the spring. The results of some thermal loadings of the spring at various fixed lengths and mechanical loadings at various constant voltages are used to train the NN. The performance of the NN model is then evaluated for some constant weight loadings which are not learnt by the NN. Simulation results indicate that compared to other neural network structures, the proposed structure learns the behavior of the SMA spring faster (in less iteration). Moreover, it provides a more general model, i.e. this NN model effectively estimates the output force for almost all possible loadings.

Słowa kluczowe

EN

artificial neural networks; smart materials; shape memory alloy springs

• Wydawca: Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
• Czasopismo: Journal of Theoretical and Applied Mechanics
• Rocznik: 2013
• Tom: Vol. 51 nr 3
• Strony: 711--718
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Kardan I

• Amirkabir University of Technology, Mechanical Engineering Departmrnt, Tehran, Iran

autor

Abiri R.

• Amirkabir University of Technology, Mechanical Engineering Departmrnt, Tehran, Iran

autor

Kabganian M.

• Amirkabir University of Technology, Mechanical Engineering Departmrnt, Tehran, Iran

autor

Vahabi M.

• Amirkabir University of Technology, Mechanical Engineering Departmrnt, Tehran, Iran

Bibliografia

• 1. Aguiar R.A.A., Savi M.A., Pacheco P.M.C.L., 2010, Experimental and numerical investigations of shape memory alloy helical springs, Smart Materials and Structures, 19, 025008
• 2. Asua E., Etxebarria V., Garcia-Arribas A., 2008, Neural network-based micropositioning control of smart shape memory alloy actuators, Engineering Applications of Artificial Intelligence, 21, 5, 796-804
• 3. Brinson L.C., 1993, One-dimensional constitutive behavior of shape memory alloys: thermomechanical derivation with non-constant material functions and redefined martensite internal variable, Journal of Intelligent Material Systems and Structures, 4, 2, 229-242
• 4. Demuth H., Beale M., 2002, Neural Network Toolbox for Use with MATLAB, User’s Guide Version 6.5, The Math Works, Inc., Natick, MA
• 5. Gao X., Qiao R., Brinson L.C., 2007, Phase diagram kinetics for shape memory alloys: a robust finite element implementation, Smart Materials and Structures, 16, 6, 2102-2115
• 6. Hadi A.R., Yousefi-Koma A., Moghaddam M.M., Elahinia M., Ghazavi A., 2010, Developing a novel SMA-actuated robotic module, Sensors and Actuators A: Physical, 162, 1, 72-81
• 7. Kim B., Lee M.G., Lee Y.P., Kim Y., Lee G., 2006, An earthworm-like micro robot Rusing shape memory alloy actuator, Sensors and Actuators A: Physical, 125, 2, 429-437
• 8. Lan C.C., Lin C.M., Fan C.H., 2011, A self-sensing microgripper module with wide handling ranges, IEEE/ASME Transactions On Mechatronics, 16, 1, 141-150
• 9. Lee H.J., Lee J.J., 2000, Evaluation of the characteristics of a shape memory alloy spring actuator, Smart Materials and Structures, 9, 6, 817-823
• 10. Liang C., Rogers C.A., 1997, Design of shape memory alloy springs with applications in vibration control, Journal of Intelligent Material Systems and Structures, 8, 4, 314-322
• 11. Song G., Chaudhry V., Batur C., 2003, Precision tracking control of shape memory Allom actuators using neural networks and a sliding-mode based robust controller, Smart Materials and Structures, 12, 2, 223-231
• 12. Sun Q.P., Hwang K.C., 1993, Micromechanics modelling for the constitutive behavior of polycrystalline shape memory alloys, part I: Derivation of general relations, Journal of the Mechanics and Physics of Solids, 41, 1, 1-17
• 13. Sun Q.P., Hwang K.C., 1993,Micromechanics modelling for the constitutive behavior of polycrystalline shape memory alloys, part II: Study of the individual phenomena, Journal of the Mechanics and Physics of Solids, 41, 1,19-33
• 14. Tanaka K., 1986, A thermomechanical sketch of shape memory effect: One-dimensional tensile behavior, Res Mechanica, 18, 3, 251-263
• 15. Zhang X.D., Rogers C.A. Liang C., 1997, Modelling of the two-way shape memory effect, Journal of Intelligent Material Systems and Structures, 8, 4, 353-362